Foreign experience in the use of artificial intelligence in the judiciary
Bibliographic record
Abstract
The article explores the features of using artificial intelligence in the judiciary of foreign countries. It emphasizes that the relevance of this issue is driven by the digitalization of judicial systems, which holds the potential to optimize court proceedings while simultaneously raising a number of ethical, legal, and other challenges. It is established that despite the widespread use of artificial intelligence in the judiciary across various countries, there is no unified model for the legal regulation or implementation of such technologies. The study identifies the United Kingdom and New Zealand as countries that have adopted a targeted approach to legal regulation of artificial intelligence in the judiciary, evidenced by the development of specific guidelines for judges on the use of artificial intelligence. A general approach to artificial intelligence regulation has been applied in the European Union, as reflected in the EU Artificial Intelligence Act and the United States, where the National Artificial Intelligence Initiative Act came into force in 2020. It has been established that, regardless of the existence or absence of legal regulation (whether specific or general), many countries operate online courts that resolve cases based on algorithms (e.g., the United Kingdom, China), and courts use AI-based systems and programs, including ChatGPT, as a basis for judicial reasoning (e.g., in India).. The article examines artificial intelligence tools such as the assistive system OLGA in Germany, PAS and COMPAS programs in the USA, Prometea in Argentina, and VICTOR in Brazil etc., which are used for metadata analysis, assisting judges in determining preventive measures, conducting preliminary case reviews, and predicting case outcomes. Special attention is given to China’s advanced use of artificial intelligence in the judiciary, particularly through its “smart court” concept. The article also identifies a group of countries, such as Canada and France, that have legally prohibited the use of artificial intelligence technologies in the judiciary. It concludes that despite varying approaches to the use of artificial intelligence in justice systems across the globe, the ongoing digitalization process necessitates the search for and adaptation of proven technical and organizational solutions tailored to the context of each country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".